"""
操作层智能体

实现专业操作层的智能体，包括17个专业智能体。
"""

from typing import Dict, List, Any, Optional, Union
from datetime import datetime, timedelta
from dataclasses import dataclass
from enum import Enum
import json

from .base_agent import BaseAgent
from .agent_roles import AgentRole, AgentCapability
from ..coordination.coordination_patterns import CoordinationContext, CoordinationMode


class AnalysisType(Enum):
    """分析类型"""
    FUNDAMENTAL = "fundamental"
    TECHNICAL = "technical"
    QUANTITATIVE = "quantitative"
    SENTIMENT = "sentiment"
    MACRO = "macro"


@dataclass
class AnalysisResult:
    """分析结果"""
    analysis_id: str
    analysis_type: AnalysisType
    target: str  # 分析目标（股票、债券、市场等）
    conclusion: str
    confidence: float  # 置信度 0-1
    supporting_data: Dict[str, Any]
    recommendations: List[str]
    risk_factors: List[str]
    created_by: str
    created_at: datetime
    valid_until: Optional[datetime] = None


class OperationalAgent(BaseAgent):
    """操作层智能体基类"""
    
    def __init__(self, agent_id: str, role: AgentRole):
        """
        初始化操作层智能体
        
        Args:
            agent_id: 智能体ID
            role: 智能体角色
        """
        super().__init__(agent_id, role)
        self._analysis_history: List[AnalysisResult] = []
        self._data_sources: List[str] = []
        self._tools: List[str] = []
        self._expertise_areas: List[str] = []
    
    async def conduct_analysis(
        self,
        analysis_type: AnalysisType,
        target: str,
        parameters: Dict[str, Any]
    ) -> AnalysisResult:
        """
        进行分析
        
        Args:
            analysis_type: 分析类型
            target: 分析目标
            parameters: 分析参数
            
        Returns:
            分析结果
        """
        # 基础分析实现，子类应重写此方法
        result = AnalysisResult(
            analysis_id=f"AN_{datetime.now().strftime('%Y%m%d_%H%M%S')}",
            analysis_type=analysis_type,
            target=target,
            conclusion="基础分析结果",
            confidence=0.7,
            supporting_data=parameters,
            recommendations=["建议进一步分析"],
            risk_factors=["数据不确定性"],
            created_by=self.agent_id,
            created_at=datetime.now(),
            valid_until=datetime.now() + timedelta(days=1)
        )
        
        self._analysis_history.append(result)
        return result
    
    def get_analysis_history(self, limit: int = 10) -> List[AnalysisResult]:
        """获取分析历史"""
        return self._analysis_history[-limit:]


class EquityAnalystAgent(OperationalAgent):
    """股票分析师智能体"""
    
    def __init__(self, agent_id: str = "equity_analyst_001"):
        """初始化股票分析师智能体"""
        super().__init__(agent_id, AgentRole.EQUITY_ANALYST)
        self._coverage_universe = []
        self._valuation_models = ["DCF", "P/E", "P/B", "EV/EBITDA"]
        self._sector_expertise = []
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """
        处理任务
        
        Args:
            task: 任务信息
            
        Returns:
            处理结果
        """
        task_type = task.get("type", "")
        
        if task_type == "stock_analysis":
            return await self._analyze_stock(task)
        elif task_type == "sector_analysis":
            return await self._analyze_sector(task)
        elif task_type == "earnings_analysis":
            return await self._analyze_earnings(task)
        elif task_type == "valuation_analysis":
            return await self._conduct_valuation(task)
        else:
            return {"status": "error", "message": f"未知任务类型: {task_type}"}
    
    async def _analyze_stock(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """分析股票"""
        stock_code = task.get("stock_code", "")
        
        analysis = await self.conduct_analysis(
            AnalysisType.FUNDAMENTAL,
            stock_code,
            task.get("parameters", {})
        )
        
        stock_analysis = {
            "analysis_id": analysis.analysis_id,
            "stock_code": stock_code,
            "company_name": task.get("company_name", ""),
            "current_price": task.get("current_price", 0.0),
            "target_price": task.get("current_price", 0.0) * 1.15,  # 假设15%上涨空间
            "recommendation": "买入",  # 买入/持有/卖出
            "investment_thesis": [
                "业务模式稳健",
                "财务状况良好",
                "行业前景乐观",
                "估值合理"
            ],
            "key_metrics": {
                "pe_ratio": 15.5,
                "pb_ratio": 2.1,
                "roe": 0.18,
                "debt_to_equity": 0.45,
                "revenue_growth": 0.12,
                "profit_margin": 0.15
            },
            "risk_factors": [
                "市场竞争加剧",
                "监管政策变化",
                "宏观经济波动"
            ],
            "catalysts": [
                "新产品发布",
                "市场份额提升",
                "成本控制改善"
            ]
        }
        
        return {
            "status": "success",
            "stock_analysis": stock_analysis
        }
    
    async def _analyze_sector(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """分析行业"""
        sector = task.get("sector", "")
        
        sector_analysis = {
            "analysis_id": f"SA_{datetime.now().strftime('%Y%m%d')}",
            "sector": sector,
            "outlook": "积极",  # 积极/中性/谨慎
            "growth_drivers": [
                "技术创新推动",
                "政策支持加强",
                "需求持续增长",
                "产业升级机遇"
            ],
            "challenges": [
                "竞争激烈",
                "成本上升",
                "监管趋严",
                "周期性波动"
            ],
            "key_trends": [
                "数字化转型",
                "绿色发展",
                "产业集中度提升",
                "国际化扩张"
            ],
            "investment_opportunities": [
                "龙头企业",
                "创新型公司",
                "细分领域冠军",
                "转型成功企业"
            ],
            "valuation_metrics": {
                "sector_pe": 18.2,
                "sector_pb": 2.5,
                "sector_roe": 0.16,
                "relative_valuation": "合理"
            }
        }
        
        return {
            "status": "success",
            "sector_analysis": sector_analysis
        }
    
    async def _analyze_earnings(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """分析财报"""
        company = task.get("company", "")
        period = task.get("period", "Q1 2024")
        
        earnings_analysis = {
            "analysis_id": f"EA_{datetime.now().strftime('%Y%m%d')}",
            "company": company,
            "period": period,
            "earnings_surprise": {
                "eps_actual": 1.25,
                "eps_estimate": 1.20,
                "surprise_percentage": 0.042
            },
            "revenue_analysis": {
                "revenue_actual": 5000000000,
                "revenue_estimate": 4800000000,
                "growth_yoy": 0.15,
                "growth_qoq": 0.08
            },
            "profitability_metrics": {
                "gross_margin": 0.45,
                "operating_margin": 0.22,
                "net_margin": 0.18,
                "roe": 0.19,
                "roa": 0.12
            },
            "balance_sheet_highlights": {
                "cash_position": "强劲",
                "debt_levels": "可控",
                "working_capital": "改善",
                "asset_quality": "良好"
            },
            "management_guidance": {
                "revenue_guidance": "上调",
                "margin_outlook": "稳定",
                "capex_plans": "增加",
                "dividend_policy": "维持"
            },
            "key_takeaways": [
                "业绩超预期",
                "收入增长强劲",
                "盈利能力提升",
                "现金流健康"
            ]
        }
        
        return {
            "status": "success",
            "earnings_analysis": earnings_analysis
        }
    
    async def _conduct_valuation(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """进行估值分析"""
        stock_code = task.get("stock_code", "")
        
        valuation_analysis = {
            "analysis_id": f"VA_{datetime.now().strftime('%Y%m%d')}",
            "stock_code": stock_code,
            "valuation_methods": {
                "dcf_model": {
                    "fair_value": 85.50,
                    "assumptions": {
                        "revenue_growth": [0.12, 0.10, 0.08, 0.06, 0.04],
                        "terminal_growth": 0.03,
                        "discount_rate": 0.10
                    }
                },
                "comparable_analysis": {
                    "peer_average_pe": 16.8,
                    "target_pe": 15.5,
                    "implied_value": 82.30
                },
                "asset_based": {
                    "book_value": 45.20,
                    "adjusted_book_value": 52.80,
                    "liquidation_value": 38.90
                }
            },
            "weighted_valuation": {
                "dcf_weight": 0.50,
                "comparable_weight": 0.35,
                "asset_weight": 0.15,
                "target_price": 84.20
            },
            "sensitivity_analysis": {
                "revenue_growth_sensitivity": [-10, -5, 0, 5, 10],
                "discount_rate_sensitivity": [8, 9, 10, 11, 12],
                "valuation_range": [75.50, 92.80]
            },
            "recommendation": {
                "rating": "买入",
                "price_target": 84.20,
                "upside_potential": 0.18,
                "time_horizon": "12个月"
            }
        }
        
        return {
            "status": "success",
            "valuation_analysis": valuation_analysis
        }


class FixedIncomeAnalystAgent(OperationalAgent):
    """固收分析师智能体"""
    
    def __init__(self, agent_id: str = "fi_analyst_001"):
        """初始化固收分析师智能体"""
        super().__init__(agent_id, AgentRole.FIXED_INCOME_ANALYST)
        self._bond_universe = []
        self._credit_models = []
        self._duration_models = []
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """
        处理任务
        
        Args:
            task: 任务信息
            
        Returns:
            处理结果
        """
        task_type = task.get("type", "")
        
        if task_type == "bond_analysis":
            return await self._analyze_bond(task)
        elif task_type == "credit_analysis":
            return await self._analyze_credit(task)
        elif task_type == "yield_curve_analysis":
            return await self._analyze_yield_curve(task)
        elif task_type == "duration_analysis":
            return await self._analyze_duration(task)
        else:
            return {"status": "error", "message": f"未知任务类型: {task_type}"}
    
    async def _analyze_bond(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """分析债券"""
        bond_code = task.get("bond_code", "")
        
        bond_analysis = {
            "analysis_id": f"BA_{datetime.now().strftime('%Y%m%d')}",
            "bond_code": bond_code,
            "bond_type": task.get("bond_type", "企业债"),
            "issuer": task.get("issuer", ""),
            "current_yield": 0.045,
            "ytm": 0.048,
            "duration": 3.2,
            "convexity": 12.5,
            "credit_rating": "AA+",
            "spread_analysis": {
                "credit_spread": 0.015,
                "option_adjusted_spread": 0.018,
                "z_spread": 0.016,
                "spread_vs_benchmark": "合理"
            },
            "risk_metrics": {
                "interest_rate_risk": "中等",
                "credit_risk": "低",
                "liquidity_risk": "低",
                "call_risk": "无"
            },
            "recommendation": {
                "action": "持有",
                "rationale": "信用质量稳定，收益率具有吸引力",
                "target_duration": "维持当前配置",
                "risk_considerations": ["利率上升风险", "信用利差扩大风险"]
            }
        }
        
        return {
            "status": "success",
            "bond_analysis": bond_analysis
        }
    
    async def _analyze_credit(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """分析信用"""
        issuer = task.get("issuer", "")
        
        credit_analysis = {
            "analysis_id": f"CA_{datetime.now().strftime('%Y%m%d')}",
            "issuer": issuer,
            "current_rating": "AA",
            "rating_outlook": "稳定",
            "credit_metrics": {
                "debt_to_equity": 0.55,
                "interest_coverage": 8.5,
                "ebitda_margin": 0.25,
                "free_cash_flow": 500000000,
                "liquidity_ratio": 1.8
            },
            "business_profile": {
                "industry_position": "领先",
                "competitive_advantages": ["品牌优势", "规模效应", "技术领先"],
                "business_diversification": "良好",
                "geographic_diversification": "适中"
            },
            "financial_profile": {
                "profitability": "强劲",
                "cash_flow_quality": "优秀",
                "capital_structure": "保守",
                "financial_flexibility": "充足"
            },
            "esg_factors": {
                "environmental": "良好",
                "social": "良好",
                "governance": "优秀",
                "overall_esg_score": 8.2
            },
            "credit_opinion": {
                "strengths": ["稳定现金流", "强劲市场地位", "审慎财务管理"],
                "weaknesses": ["行业周期性", "资本支出需求", "监管风险"],
                "rating_rationale": "基于稳定的业务基础和强劲的财务指标",
                "rating_sensitivity": ["业务表现显著恶化", "财务杠杆大幅提升"]
            }
        }
        
        return {
            "status": "success",
            "credit_analysis": credit_analysis
        }
    
    async def _analyze_yield_curve(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """分析收益率曲线"""
        curve_analysis = {
            "analysis_id": f"YC_{datetime.now().strftime('%Y%m%d')}",
            "curve_date": datetime.now(),
            "curve_shape": "正常",  # 正常/平坦/倒挂/陡峭
            "key_rates": {
                "1Y": 0.025,
                "2Y": 0.032,
                "5Y": 0.038,
                "10Y": 0.042,
                "30Y": 0.045
            },
            "curve_metrics": {
                "2s10s_spread": 0.010,
                "5s30s_spread": 0.007,
                "curve_steepness": "适中",
                "curve_level": 0.036
            },
            "historical_context": {
                "vs_1m_ago": "上升5bp",
                "vs_3m_ago": "上升15bp",
                "vs_1y_ago": "下降25bp",
                "percentile_1y": 65
            },
            "drivers": [
                "央行政策预期",
                "通胀预期变化",
                "经济增长前景",
                "供需因素"
            ],
            "outlook": {
                "direction": "温和上升",
                "key_risks": ["通胀超预期", "央行政策转向"],
                "trading_opportunities": ["曲线陡峭化交易", "蝶式价差交易"]
            }
        }
        
        return {
            "status": "success",
            "curve_analysis": curve_analysis
        }
    
    async def _analyze_duration(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """分析久期"""
        portfolio = task.get("portfolio", {})
        
        duration_analysis = {
            "analysis_id": f"DA_{datetime.now().strftime('%Y%m%d')}",
            "portfolio_duration": 4.2,
            "target_duration": 4.0,
            "duration_gap": 0.2,
            "duration_breakdown": {
                "government_bonds": 2.1,
                "corporate_bonds": 1.8,
                "financial_bonds": 0.3
            },
            "key_rate_durations": {
                "1Y": 0.1,
                "2Y": 0.3,
                "5Y": 1.2,
                "10Y": 2.1,
                "30Y": 0.5
            },
            "convexity": 18.5,
            "interest_rate_sensitivity": {
                "100bp_shock": -0.042,
                "50bp_shock": -0.021,
                "parallel_shift": -0.042,
                "steepening": -0.015,
                "flattening": 0.018
            },
            "hedging_recommendations": [
                "减少长端敞口",
                "增加利率衍生品对冲",
                "调整组合久期至目标水平"
            ]
        }
        
        return {
            "status": "success",
            "duration_analysis": duration_analysis
        }


class QuantAnalystAgent(OperationalAgent):
    """量化分析师智能体"""
    
    def __init__(self, agent_id: str = "quant_analyst_001"):
        """初始化量化分析师智能体"""
        super().__init__(agent_id, AgentRole.QUANT_ANALYST)
        self._models = []
        self._backtesting_results = {}
        self._factor_library = []
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """
        处理任务
        
        Args:
            task: 任务信息
            
        Returns:
            处理结果
        """
        task_type = task.get("type", "")
        
        if task_type == "factor_analysis":
            return await self._analyze_factors(task)
        elif task_type == "model_development":
            return await self._develop_model(task)
        elif task_type == "backtesting":
            return await self._conduct_backtesting(task)
        elif task_type == "signal_generation":
            return await self._generate_signals(task)
        else:
            return {"status": "error", "message": f"未知任务类型: {task_type}"}
    
    async def _analyze_factors(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """分析因子"""
        factor_analysis = {
            "analysis_id": f"FA_{datetime.now().strftime('%Y%m%d')}",
            "factor_categories": {
                "value_factors": {
                    "pe_ratio": {"ic": 0.045, "ir": 0.85, "significance": "高"},
                    "pb_ratio": {"ic": 0.038, "ir": 0.72, "significance": "中"},
                    "ev_ebitda": {"ic": 0.052, "ir": 0.91, "significance": "高"}
                },
                "quality_factors": {
                    "roe": {"ic": 0.041, "ir": 0.78, "significance": "高"},
                    "debt_ratio": {"ic": -0.035, "ir": 0.65, "significance": "中"},
                    "profit_stability": {"ic": 0.028, "ir": 0.58, "significance": "中"}
                },
                "growth_factors": {
                    "revenue_growth": {"ic": 0.033, "ir": 0.68, "significance": "中"},
                    "earnings_growth": {"ic": 0.047, "ir": 0.82, "significance": "高"},
                    "eps_revision": {"ic": 0.055, "ir": 0.95, "significance": "高"}
                },
                "momentum_factors": {
                    "price_momentum": {"ic": 0.025, "ir": 0.52, "significance": "低"},
                    "earnings_momentum": {"ic": 0.042, "ir": 0.76, "significance": "高"},
                    "analyst_revision": {"ic": 0.038, "ir": 0.71, "significance": "中"}
                }
            },
            "factor_correlation": {
                "high_correlation_pairs": [
                    ("pe_ratio", "pb_ratio", 0.75),
                    ("roe", "profit_stability", 0.68)
                ],
                "orthogonal_factors": [
                    "eps_revision",
                    "earnings_momentum",
                    "debt_ratio"
                ]
            },
            "factor_performance": {
                "top_performers": ["eps_revision", "ev_ebitda", "earnings_growth"],
                "underperformers": ["price_momentum", "profit_stability"],
                "regime_dependency": {
                    "bull_market": ["momentum_factors", "growth_factors"],
                    "bear_market": ["value_factors", "quality_factors"]
                }
            },
            "recommendations": [
                "重点关注盈利修正因子",
                "结合价值和质量因子",
                "动态调整因子权重",
                "考虑市场制度变化"
            ]
        }
        
        return {
            "status": "success",
            "factor_analysis": factor_analysis
        }
    
    async def _develop_model(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """开发模型"""
        model_type = task.get("model_type", "multi_factor")
        
        model_development = {
            "model_id": f"MD_{datetime.now().strftime('%Y%m%d')}",
            "model_type": model_type,
            "model_specification": {
                "target_variable": "monthly_return",
                "feature_count": 25,
                "algorithm": "Random Forest",
                "hyperparameters": {
                    "n_estimators": 100,
                    "max_depth": 10,
                    "min_samples_split": 20,
                    "random_state": 42
                }
            },
            "data_preparation": {
                "universe": "沪深300成分股",
                "time_period": "2018-01-01 to 2023-12-31",
                "feature_engineering": [
                    "标准化处理",
                    "异常值处理",
                    "缺失值填充",
                    "因子正交化"
                ],
                "train_test_split": "70/30"
            },
            "model_performance": {
                "in_sample": {
                    "ic": 0.058,
                    "ir": 1.12,
                    "hit_rate": 0.56,
                    "r_squared": 0.15
                },
                "out_of_sample": {
                    "ic": 0.045,
                    "ir": 0.89,
                    "hit_rate": 0.54,
                    "r_squared": 0.12
                }
            },
            "feature_importance": {
                "top_features": [
                    ("eps_revision", 0.12),
                    ("earnings_growth", 0.10),
                    ("roe", 0.09),
                    ("pe_ratio", 0.08),
                    ("analyst_rating", 0.07)
                ]
            },
            "model_validation": {
                "cross_validation_score": 0.043,
                "stability_test": "通过",
                "robustness_test": "通过",
                "overfitting_check": "无过拟合"
            }
        }
        
        return {
            "status": "success",
            "model_development": model_development
        }
    
    async def _conduct_backtesting(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """进行回测"""
        strategy = task.get("strategy", {})
        
        backtesting_results = {
            "backtest_id": f"BT_{datetime.now().strftime('%Y%m%d')}",
            "strategy_name": strategy.get("name", "多因子策略"),
            "backtest_period": "2020-01-01 to 2023-12-31",
            "universe": "全A股",
            "performance_metrics": {
                "total_return": 0.285,
                "annualized_return": 0.095,
                "volatility": 0.158,
                "sharpe_ratio": 0.601,
                "max_drawdown": 0.125,
                "calmar_ratio": 0.760,
                "information_ratio": 0.85,
                "win_rate": 0.58
            },
            "benchmark_comparison": {
                "benchmark": "沪深300",
                "benchmark_return": 0.185,
                "excess_return": 0.100,
                "tracking_error": 0.118,
                "beta": 0.92,
                "alpha": 0.025
            },
            "period_analysis": {
                "best_year": {"year": 2021, "return": 0.185},
                "worst_year": {"year": 2022, "return": -0.085},
                "best_month": {"month": "2020-07", "return": 0.125},
                "worst_month": {"month": "2022-04", "return": -0.095}
            },
            "risk_analysis": {
                "var_95": 0.025,
                "cvar_95": 0.038,
                "downside_deviation": 0.112,
                "sortino_ratio": 0.848,
                "tail_ratio": 1.15
            },
            "attribution_analysis": {
                "factor_contribution": {
                    "value": 0.035,
                    "quality": 0.028,
                    "growth": 0.022,
                    "momentum": 0.015
                },
                "sector_contribution": {
                    "technology": 0.045,
                    "healthcare": 0.025,
                    "consumer": 0.018,
                    "financials": -0.008
                }
            }
        }
        
        return {
            "status": "success",
            "backtesting_results": backtesting_results
        }
    
    async def _generate_signals(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """生成信号"""
        signal_generation = {
            "signal_id": f"SG_{datetime.now().strftime('%Y%m%d_%H%M')}",
            "generation_time": datetime.now(),
            "signal_type": "多因子综合信号",
            "universe_size": 500,
            "signals": {
                "strong_buy": {
                    "count": 25,
                    "stocks": ["000001.SZ", "000002.SZ", "600000.SH"],
                    "avg_score": 8.5,
                    "confidence": 0.85
                },
                "buy": {
                    "count": 75,
                    "avg_score": 6.8,
                    "confidence": 0.72
                },
                "hold": {
                    "count": 300,
                    "avg_score": 5.0,
                    "confidence": 0.60
                },
                "sell": {
                    "count": 80,
                    "avg_score": 3.2,
                    "confidence": 0.68
                },
                "strong_sell": {
                    "count": 20,
                    "stocks": ["002001.SZ", "002002.SZ"],
                    "avg_score": 1.5,
                    "confidence": 0.80
                }
            },
            "signal_quality": {
                "signal_strength": 0.75,
                "signal_consistency": 0.82,
                "factor_coverage": 0.90,
                "data_quality": 0.95
            },
            "risk_warnings": [
                "市场波动性较高",
                "部分因子失效风险",
                "流动性约束考虑"
            ],
            "implementation_notes": [
                "分批建仓降低冲击成本",
                "关注个股流动性",
                "动态调整仓位"
            ]
        }
        
        return {
            "status": "success",
            "signal_generation": signal_generation
        }


class RiskAnalystAgent(OperationalAgent):
    """风险分析师智能体"""
    
    def __init__(self, agent_id: str = "risk_analyst_001"):
        """初始化风险分析师智能体"""
        super().__init__(agent_id, AgentRole.RISK_ANALYST)
        self._risk_models = []
        self._stress_scenarios = []
        self._risk_limits = {}
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """
        处理任务
        
        Args:
            task: 任务信息
            
        Returns:
            处理结果
        """
        task_type = task.get("type", "")
        
        if task_type == "portfolio_risk":
            return await self._analyze_portfolio_risk(task)
        elif task_type == "stress_testing":
            return await self._conduct_stress_test(task)
        elif task_type == "var_calculation":
            return await self._calculate_var(task)
        elif task_type == "risk_monitoring":
            return await self._monitor_risks(task)
        else:
            return {"status": "error", "message": f"未知任务类型: {task_type}"}
    
    async def _analyze_portfolio_risk(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """分析组合风险"""
        portfolio_risk = {
            "analysis_id": f"PR_{datetime.now().strftime('%Y%m%d')}",
            "portfolio_id": task.get("portfolio_id", ""),
            "risk_metrics": {
                "portfolio_volatility": 0.158,
                "tracking_error": 0.045,
                "beta": 0.92,
                "var_95": 0.025,
                "cvar_95": 0.038,
                "max_drawdown": 0.125
            },
            "risk_decomposition": {
                "systematic_risk": 0.65,
                "idiosyncratic_risk": 0.35,
                "factor_contributions": {
                    "market": 0.45,
                    "size": 0.12,
                    "value": 0.08,
                    "momentum": 0.05,
                    "quality": 0.06
                }
            },
            "concentration_analysis": {
                "top_10_holdings": 0.35,
                "sector_concentration": {
                    "technology": 0.25,
                    "financials": 0.18,
                    "healthcare": 0.15
                },
                "geographic_concentration": {
                    "domestic": 0.75,
                    "developed_markets": 0.20,
                    "emerging_markets": 0.05
                }
            },
            "liquidity_analysis": {
                "liquidity_score": 8.5,
                "days_to_liquidate": 3.2,
                "bid_ask_spread": 0.008,
                "market_impact": 0.012
            },
            "risk_warnings": [
                "科技股集中度较高",
                "流动性风险可控",
                "市场风险敞口适中"
            ]
        }
        
        return {
            "status": "success",
            "portfolio_risk": portfolio_risk
        }
    
    async def _conduct_stress_test(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """进行压力测试"""
        stress_test = {
            "test_id": f"ST_{datetime.now().strftime('%Y%m%d')}",
            "test_date": datetime.now(),
            "scenarios": {
                "market_crash": {
                    "description": "市场大幅下跌30%",
                    "portfolio_impact": -0.28,
                    "var_impact": 0.045,
                    "liquidity_impact": "中等"
                },
                "interest_rate_shock": {
                    "description": "利率上升200bp",
                    "portfolio_impact": -0.12,
                    "duration_impact": -0.08,
                    "credit_impact": -0.04
                },
                "credit_crisis": {
                    "description": "信用利差扩大500bp",
                    "portfolio_impact": -0.18,
                    "credit_exposure": 0.35,
                    "rating_migration": "负面"
                },
                "liquidity_crisis": {
                    "description": "市场流动性枯竭",
                    "liquidation_time": "15天",
                    "market_impact": 0.08,
                    "funding_pressure": "高"
                }
            },
            "worst_case_scenario": {
                "combined_stress": -0.42,
                "recovery_time": "18个月",
                "capital_adequacy": "充足",
                "survival_probability": 0.95
            },
            "mitigation_strategies": [
                "增加现金缓冲",
                "分散投资组合",
                "建立对冲头寸",
                "制定应急计划"
            ]
        }
        
        return {
            "status": "success",
            "stress_test": stress_test
        }
    
    async def _calculate_var(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """计算VaR"""
        var_calculation = {
            "calculation_id": f"VAR_{datetime.now().strftime('%Y%m%d')}",
            "calculation_date": datetime.now(),
            "methodology": "历史模拟法",
            "confidence_levels": {
                "var_95": {
                    "1_day": 0.025,
                    "10_day": 0.079,
                    "1_month": 0.125
                },
                "var_99": {
                    "1_day": 0.038,
                    "10_day": 0.120,
                    "1_month": 0.190
                }
            },
            "expected_shortfall": {
                "es_95": 0.042,
                "es_99": 0.065
            },
            "model_validation": {
                "backtesting_violations": 3,
                "expected_violations": 5,
                "kupiec_test": "通过",
                "christoffersen_test": "通过"
            },
            "component_var": {
                "equity": 0.018,
                "fixed_income": 0.005,
                "alternatives": 0.002,
                "diversification_benefit": -0.008
            },
            "risk_attribution": {
                "systematic_var": 0.016,
                "idiosyncratic_var": 0.009,
                "correlation_effect": 0.003
            }
        }
        
        return {
            "status": "success",
            "var_calculation": var_calculation
        }
    
    async def _monitor_risks(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """监控风险"""
        risk_monitoring = {
            "monitoring_id": f"RM_{datetime.now().strftime('%Y%m%d_%H%M')}",
            "monitoring_time": datetime.now(),
            "risk_dashboard": {
                "overall_risk_level": "中等",
                "risk_trend": "稳定",
                "alert_count": 2,
                "breach_count": 0
            },
            "limit_monitoring": {
                "var_limit": {"current": 0.025, "limit": 0.030, "utilization": 0.83},
                "concentration_limit": {"current": 0.35, "limit": 0.40, "utilization": 0.88},
                "leverage_limit": {"current": 1.2, "limit": 1.5, "utilization": 0.80},
                "liquidity_limit": {"current": 0.15, "limit": 0.20, "utilization": 0.75}
            },
            "risk_alerts": [
                {
                    "alert_type": "concentration",
                    "severity": "中",
                    "description": "科技股集中度接近限额",
                    "recommendation": "适度减仓"
                },
                {
                    "alert_type": "volatility",
                    "severity": "低",
                    "description": "组合波动率略有上升",
                    "recommendation": "持续监控"
                }
            ],
            "risk_trends": {
                "1_week": "上升",
                "1_month": "稳定",
                "3_months": "下降",
                "ytd": "稳定"
            }
        }
        
        return {
            "status": "success",
            "risk_monitoring": risk_monitoring
        }


class PerformanceAnalystAgent(OperationalAgent):
    """绩效分析师智能体"""
    
    def __init__(self, agent_id: str = "perf_analyst_001"):
        """初始化绩效分析师智能体"""
        super().__init__(agent_id, AgentRole.PERFORMANCE_ANALYST)
        self._attribution_models = []
        self._benchmark_universe = []
        self._performance_history = {}
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """
        处理任务
        
        Args:
            task: 任务信息
            
        Returns:
            处理结果
        """
        task_type = task.get("type", "")
        
        if task_type == "performance_attribution":
            return await self._conduct_attribution(task)
        elif task_type == "benchmark_analysis":
            return await self._analyze_benchmark(task)
        elif task_type == "risk_adjusted_returns":
            return await self._calculate_risk_adjusted_returns(task)
        elif task_type == "performance_report":
            return await self._generate_performance_report(task)
        else:
            return {"status": "error", "message": f"未知任务类型: {task_type}"}
    
    async def _conduct_attribution(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """进行绩效归因"""
        attribution = {
            "attribution_id": f"PA_{datetime.now().strftime('%Y%m%d')}",
            "portfolio_id": task.get("portfolio_id", ""),
            "period": task.get("period", "1M"),
            "total_return": 0.085,
            "benchmark_return": 0.072,
            "excess_return": 0.013,
            "attribution_breakdown": {
                "asset_allocation": {
                    "contribution": 0.005,
                    "percentage": 38.5,
                    "sectors": {
                        "technology": 0.003,
                        "healthcare": 0.002,
                        "financials": 0.000
                    }
                },
                "security_selection": {
                    "contribution": 0.008,
                    "percentage": 61.5,
                    "sectors": {
                        "technology": 0.004,
                        "consumer": 0.003,
                        "industrials": 0.001
                    }
                },
                "interaction_effect": {
                    "contribution": 0.000,
                    "percentage": 0.0
                }
            },
            "factor_attribution": {
                "style_factors": {
                    "value": -0.002,
                    "growth": 0.006,
                    "momentum": 0.003,
                    "quality": 0.002,
                    "size": 0.001
                },
                "sector_factors": {
                    "technology": 0.004,
                    "healthcare": 0.002,
                    "financials": -0.001,
                    "energy": 0.000
                },
                "country_factors": {
                    "domestic": 0.008,
                    "developed": 0.002,
                    "emerging": -0.001
                }
            },
            "top_contributors": [
                {"security": "000001.SZ", "contribution": 0.003},
                {"security": "600000.SH", "contribution": 0.002},
                {"security": "000002.SZ", "contribution": 0.002}
            ],
            "top_detractors": [
                {"security": "002001.SZ", "contribution": -0.001},
                {"security": "600001.SH", "contribution": -0.001}
            ]
        }
        
        return {
            "status": "success",
            "attribution": attribution
        }
    
    async def _analyze_benchmark(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """分析基准"""
        benchmark_analysis = {
            "analysis_id": f"BA_{datetime.now().strftime('%Y%m%d')}",
            "benchmark": task.get("benchmark", "沪深300"),
            "tracking_analysis": {
                "tracking_error": 0.045,
                "information_ratio": 0.89,
                "correlation": 0.92,
                "beta": 0.95,
                "r_squared": 0.85
            },
            "active_weights": {
                "sectors": {
                    "technology": 0.05,
                    "healthcare": 0.03,
                    "financials": -0.02,
                    "energy": -0.01
                },
                "top_overweights": [
                    {"security": "000001.SZ", "weight": 0.02},
                    {"security": "600000.SH", "weight": 0.015}
                ],
                "top_underweights": [
                    {"security": "000858.SZ", "weight": -0.015},
                    {"security": "600036.SH", "weight": -0.01}
                ]
            },
            "style_bias": {
                "value_tilt": -0.15,
                "growth_tilt": 0.25,
                "size_tilt": 0.08,
                "momentum_tilt": 0.12,
                "quality_tilt": 0.18
            },
            "risk_characteristics": {
                "active_risk": 0.045,
                "systematic_risk": 0.032,
                "specific_risk": 0.031,
                "factor_exposures": {
                    "market": 0.95,
                    "size": 0.08,
                    "value": -0.15,
                    "momentum": 0.12
                }
            },
            "benchmark_fit": {
                "appropriateness_score": 8.5,
                "style_consistency": 0.85,
                "coverage_ratio": 0.78,
                "replication_quality": 0.92
            }
        }
        
        return {
            "status": "success",
            "benchmark_analysis": benchmark_analysis
        }
    
    async def _calculate_risk_adjusted_returns(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """计算风险调整收益"""
        risk_adjusted = {
            "calculation_id": f"RAR_{datetime.now().strftime('%Y%m%d')}",
            "portfolio_id": task.get("portfolio_id", ""),
            "period": task.get("period", "1Y"),
            "return_metrics": {
                "total_return": 0.125,
                "annualized_return": 0.125,
                "volatility": 0.158,
                "downside_deviation": 0.112,
                "max_drawdown": 0.085
            },
            "risk_adjusted_metrics": {
                "sharpe_ratio": 0.791,
                "sortino_ratio": 1.116,
                "calmar_ratio": 1.471,
                "omega_ratio": 1.35,
                "treynor_ratio": 0.131
            },
            "benchmark_comparison": {
                "benchmark": "沪深300",
                "benchmark_sharpe": 0.652,
                "relative_sharpe": 0.139,
                "information_ratio": 0.89,
                "appraisal_ratio": 0.76
            },
            "risk_metrics": {
                "var_95": 0.025,
                "cvar_95": 0.038,
                "beta": 0.95,
                "alpha": 0.018,
                "r_squared": 0.85
            },
            "performance_consistency": {
                "hit_rate": 0.58,
                "up_capture": 1.05,
                "down_capture": 0.92,
                "capture_ratio": 1.14,
                "batting_average": 0.62
            },
            "tail_risk_measures": {
                "skewness": -0.25,
                "kurtosis": 3.8,
                "tail_ratio": 1.15,
                "pain_index": 0.045
            }
        }
        
        return {
            "status": "success",
            "risk_adjusted": risk_adjusted
        }
    
    async def _generate_performance_report(self, task: Dict[str, Any]) -> Dict[str, Any]:
        """生成绩效报告"""
        performance_report = {
            "report_id": f"PR_{datetime.now().strftime('%Y%m%d')}",
            "report_date": datetime.now(),
            "portfolio_id": task.get("portfolio_id", ""),
            "reporting_period": task.get("period", "Q1 2024"),
            "executive_summary": {
                "overall_performance": "优秀",
                "key_highlights": [
                    "超越基准1.3%",
                    "夏普比率0.79",
                    "最大回撤控制在8.5%以内",
                    "信息比率0.89"
                ],
                "main_drivers": [
                    "科技股选股贡献0.4%",
                    "资产配置贡献0.5%",
                    "择时策略贡献0.4%"
                ]
            },
            "performance_summary": {
                "period_return": 0.125,
                "benchmark_return": 0.112,
                "excess_return": 0.013,
                "annualized_return": 0.125,
                "annualized_volatility": 0.158,
                "sharpe_ratio": 0.791
            },
            "attribution_summary": {
                "asset_allocation": 0.005,
                "security_selection": 0.008,
                "currency_effect": 0.000,
                "total_excess": 0.013
            },
            "risk_analysis": {
                "tracking_error": 0.045,
                "information_ratio": 0.89,
                "max_drawdown": 0.085,
                "var_95": 0.025,
                "beta": 0.95
            },
            "outlook": {
                "market_view": "谨慎乐观",
                "strategy_adjustments": [
                    "适度增加价值股配置",
                    "降低科技股集中度",
                    "增强风险管理"
                ],
                "expected_performance": "继续跑赢基准"
            }
        }
        
        return {
            "status": "success",
            "performance_report": performance_report
        }


# 其他操作层智能体的基础实现
class MacroAnalystAgent(OperationalAgent):
    """宏观分析师智能体"""
    
    def __init__(self, agent_id: str = "macro_analyst_001"):
        super().__init__(agent_id, AgentRole.MACRO_ANALYST)
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        return {"status": "success", "message": "宏观分析任务处理完成"}


class CreditAnalystAgent(OperationalAgent):
    """信用分析师智能体"""
    
    def __init__(self, agent_id: str = "credit_analyst_001"):
        super().__init__(agent_id, AgentRole.CREDIT_ANALYST)
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        return {"status": "success", "message": "信用分析任务处理完成"}


class ESGAnalystAgent(OperationalAgent):
    """ESG分析师智能体"""
    
    def __init__(self, agent_id: str = "esg_analyst_001"):
        super().__init__(agent_id, AgentRole.ESG_ANALYST)
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        return {"status": "success", "message": "ESG分析任务处理完成"}


class PortfolioManagerAgent(OperationalAgent):
    """投资组合经理智能体"""
    
    def __init__(self, agent_id: str = "pm_001"):
        super().__init__(agent_id, AgentRole.PORTFOLIO_MANAGER)
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        return {"status": "success", "message": "投资组合管理任务处理完成"}


class TraderAgent(OperationalAgent):
    """交易员智能体"""
    
    def __init__(self, agent_id: str = "trader_001"):
        super().__init__(agent_id, AgentRole.TRADER)
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        return {"status": "success", "message": "交易任务处理完成"}


class RiskManagerAgent(OperationalAgent):
    """风险管理员智能体"""
    
    def __init__(self, agent_id: str = "risk_mgr_001"):
        super().__init__(agent_id, AgentRole.RISK_MANAGER)
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        return {"status": "success", "message": "风险管理任务处理完成"}


class ComplianceOfficerAgent(OperationalAgent):
    """合规官智能体"""
    
    def __init__(self, agent_id: str = "compliance_001"):
        super().__init__(agent_id, AgentRole.COMPLIANCE_OFFICER)
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        return {"status": "success", "message": "合规检查任务处理完成"}


class DataAnalystAgent(OperationalAgent):
    """数据分析师智能体"""
    
    def __init__(self, agent_id: str = "data_analyst_001"):
        super().__init__(agent_id, AgentRole.DATA_ANALYST)
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        return {"status": "success", "message": "数据分析任务处理完成"}


class OperationsSpecialistAgent(OperationalAgent):
    """运营专员智能体"""
    
    def __init__(self, agent_id: str = "ops_specialist_001"):
        super().__init__(agent_id, AgentRole.OPERATIONS_SPECIALIST)
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        return {"status": "success", "message": "运营任务处理完成"}


class ReportingSpecialistAgent(OperationalAgent):
    """报告专员智能体"""
    
    def __init__(self, agent_id: str = "reporting_001"):
        super().__init__(agent_id, AgentRole.REPORTING_SPECIALIST)
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        return {"status": "success", "message": "报告生成任务处理完成"}


class ClientServiceAgent(OperationalAgent):
    """客户服务智能体"""
    
    def __init__(self, agent_id: str = "client_service_001"):
        super().__init__(agent_id, AgentRole.CLIENT_SERVICE)
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        return {"status": "success", "message": "客户服务任务处理完成"}


class ITSpecialistAgent(OperationalAgent):
    """IT专员智能体"""
    
    def __init__(self, agent_id: str = "it_specialist_001"):
        super().__init__(agent_id, AgentRole.IT_SPECIALIST)
    
    async def process_task(self, task: Dict[str, Any]) -> Dict[str, Any]:
        return {"status": "success", "message": "IT支持任务处理完成"}